{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/adamw/papers/2","list_of":"/method/adamw","method":"AdamW","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":206,"counts":{"archive_papers_tagged":206,"with_a_code_link":114,"where_syntology_ran_a_sample":43,"not_listed_spam_title":0,"listed":206,"listed_where_code_ran":43,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":38,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":38,"listed_every_run_a_failure_of_syntologys_instrument":5,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/adamw","prev":"/method/adamw","next":"/method/adamw/papers/3","papers":[{"paper":"/paper/llama-2-open-foundation-and-fine-tuned-chat","slug":"llama-2-open-foundation-and-fine-tuned-chat","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","date":"2023-07-18","arxiv_id":"2307.09288","n_code_links":19,"syntology":{"ran":33,"of":52,"n_ran_checked":22,"n_instrument":11,"unverified":19,"pointer_only":20,"phrase":"33 ran (of which 7 constructed an object rather than computing a result; 22 with no instrument failure: 1 honoured, 1 violated, 20 with no contract checked; 11 where Syntology's instrument failed) · 19 unverified","official":{"repos":["facebookresearch/llama"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"nowj-at-coliee-2023-multi-task-and-ensemble","title":"NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing","date":"2023-06-08","arxiv_id":"2306.04903","n_code_links":0,"syntology":null},{"paper":"/paper/using-sequences-of-life-events-to-predict","slug":"using-sequences-of-life-events-to-predict","title":"Using Sequences of Life-events to Predict Human Lives","date":"2023-06-05","arxiv_id":"2306.03009","n_code_links":2,"syntology":null},{"paper":null,"slug":"multilegalpile-a-689gb-multilingual-legal","title":"MultiLegalPile: A 689GB Multilingual Legal Corpus","date":"2023-06-03","arxiv_id":"2306.02069","n_code_links":0,"syntology":null},{"paper":"/paper/improving-energy-conserving-descent-for","slug":"improving-energy-conserving-descent-for","title":"Improving Energy Conserving Descent for Machine Learning: Theory and Practice","date":"2023-06-01","arxiv_id":"2306.00352","n_code_links":1,"syntology":null},{"paper":null,"slug":"incorporating-distributions-of-discourse","title":"Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization","date":"2023-05-26","arxiv_id":"2305.16784","n_code_links":0,"syntology":null},{"paper":"/paper/rotational-optimizers-simple-robust-dnn","slug":"rotational-optimizers-simple-robust-dnn","title":"Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks","date":"2023-05-26","arxiv_id":"2305.17212","n_code_links":2,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["epfml/req","epfml/rotational-optimizers"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/xgrad-boosting-gradient-based-optimizers-with","slug":"xgrad-boosting-gradient-based-optimizers-with","title":"XGrad: Boosting Gradient-Based Optimizers With Weight Prediction","date":"2023-05-26","arxiv_id":"2305.18240","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-summarization-of-electronic-health","title":"Neural Summarization of Electronic Health Records","date":"2023-05-24","arxiv_id":"2305.15222","n_code_links":0,"syntology":null},{"paper":null,"slug":"layer-wise-adaptive-step-sizes-for-stochastic","title":"Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning","date":"2023-05-23","arxiv_id":"2305.13664","n_code_links":0,"syntology":null},{"paper":"/paper/text-is-all-you-need-learning-language","slug":"text-is-all-you-need-learning-language","title":"Text Is All You Need: Learning Language Representations for Sequential Recommendation","date":"2023-05-23","arxiv_id":"2305.13731","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/a-general-purpose-multilingual-document","slug":"a-general-purpose-multilingual-document","title":"A General-Purpose Multilingual Document Encoder","date":"2023-05-11","arxiv_id":"2305.07016","n_code_links":1,"syntology":null},{"paper":"/paper/gaanet-ghost-auto-anchor-network-for","slug":"gaanet-ghost-auto-anchor-network-for","title":"GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark","date":"2023-05-05","arxiv_id":"2305.03425","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-covid-19-and-pneumonia","title":"Predicting COVID-19 and pneumonia complications from admission texts","date":"2023-05-05","arxiv_id":"2305.03661","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-bert-language-model-for-arabic","title":"Leveraging BERT Language Model for Arabic Long Document Classification","date":"2023-05-04","arxiv_id":"2305.03519","n_code_links":0,"syntology":null},{"paper":"/paper/unlimiformer-long-range-transformers-with","slug":"unlimiformer-long-range-transformers-with","title":"Unlimiformer: Long-Range Transformers with Unlimited Length Input","date":"2023-05-02","arxiv_id":"2305.01625","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["abertsch72/unlimiformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/customized-segment-anything-model-for-medical","slug":"customized-segment-anything-model-for-medical","title":"Customized Segment Anything Model for Medical Image Segmentation","date":"2023-04-26","arxiv_id":"2304.13785","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":3,"n_instrument":3,"unverified":3,"pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hitachinsk/samed"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/stable-and-low-precision-training-for-large","slug":"stable-and-low-precision-training-for-large","title":"Stable and low-precision training for large-scale vision-language models","date":"2023-04-25","arxiv_id":"2304.13013","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["mlfoundations/open_clip"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"domain-specific-continued-pretraining-of","title":"Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health","date":"2023-04-20","arxiv_id":"2304.10447","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-parrots-looking-for-stochastic","slug":"stochastic-parrots-looking-for-stochastic","title":"Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs","date":"2023-04-18","arxiv_id":"2304.08968","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-class-categorization-of-reasons-behind","title":"Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts","date":"2023-04-08","arxiv_id":"2304.04118","n_code_links":0,"syntology":null},{"paper":null,"slug":"lipschitzness-effect-of-a-loss-function-on","title":"Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers","date":"2023-03-29","arxiv_id":"2303.16464","n_code_links":0,"syntology":null},{"paper":null,"slug":"finding-the-needle-in-a-haystack-unsupervised","title":"Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers","date":"2023-03-14","arxiv_id":"2303.07991","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-in-hospital-meta-information-useful-for","title":"Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?","date":"2023-03-10","arxiv_id":"2303.06002","n_code_links":0,"syntology":null},{"paper":"/paper/searching-for-effective-neural-network","slug":"searching-for-effective-neural-network","title":"Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram","date":"2023-03-06","arxiv_id":"2303.02988","n_code_links":1,"syntology":null},{"paper":"/paper/gradient-norm-aware-minimization-seeks-first","slug":"gradient-norm-aware-minimization-seeks-first","title":"Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization","date":"2023-03-03","arxiv_id":"2303.03108","n_code_links":1,"syntology":null},{"paper":null,"slug":"clinical-biobert-hyperparameter-optimization","title":"Clinical BioBERT Hyperparameter Optimization using Genetic Algorithm","date":"2023-02-08","arxiv_id":"2302.03822","n_code_links":0,"syntology":null},{"paper":null,"slug":"vulaste-long-sequence-model-with-abstract","title":"VuLASTE: Long Sequence Model with Abstract Syntax Tree Embedding for vulnerability Detection","date":"2023-02-05","arxiv_id":"2302.02345","n_code_links":0,"syntology":null},{"paper":"/paper/longformer-longitudinal-transformer-for","slug":"longformer-longitudinal-transformer-for","title":"Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs","date":"2023-02-02","arxiv_id":"2302.00901","n_code_links":1,"syntology":null},{"paper":null,"slug":"weight-prediction-boosts-the-convergence-of","title":"Weight Prediction Boosts the Convergence of AdamW","date":"2023-02-01","arxiv_id":"2302.00195","n_code_links":0,"syntology":null},{"paper":"/paper/a-comparative-study-of-pretrained-language-1","slug":"a-comparative-study-of-pretrained-language-1","title":"A Comparative Study of Pretrained Language Models for Long Clinical Text","date":"2023-01-27","arxiv_id":"2301.11847","n_code_links":1,"syntology":null},{"paper":"/paper/read-the-signs-towards-invariance-to-gradient","slug":"read-the-signs-towards-invariance-to-gradient","title":"Read the Signs: Towards Invariance to Gradient Descent's Hyperparameter Initialization","date":"2023-01-24","arxiv_id":"2301.10133","n_code_links":1,"syntology":null},{"paper":"/paper/development-optimization-and-deployment-of","slug":"development-optimization-and-deployment-of","title":"Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices","date":"2023-01-18","arxiv_id":"2301.07613","n_code_links":1,"syntology":null},{"paper":"/paper/a-stochastic-proximal-polyak-step-size","slug":"a-stochastic-proximal-polyak-step-size","title":"A Stochastic Proximal Polyak Step Size","date":"2023-01-12","arxiv_id":"2301.04935","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["fabian-sp/ProxSPS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/grokking-modular-arithmetic","slug":"grokking-modular-arithmetic","title":"Grokking modular arithmetic","date":"2023-01-06","arxiv_id":"2301.02679","n_code_links":1,"syntology":null},{"paper":"/paper/mobyv2al-self-supervised-active-learning-for","slug":"mobyv2al-self-supervised-active-learning-for","title":"MoBYv2AL: Self-supervised Active Learning for Image Classification","date":"2023-01-04","arxiv_id":"2301.01531","n_code_links":1,"syntology":null},{"paper":null,"slug":"understanding-political-polarisation-using","title":"Understanding Political Polarisation using Language Models: A dataset and method","date":"2023-01-02","arxiv_id":"2301.00891","n_code_links":0,"syntology":null},{"paper":"/paper/a-general-regret-bound-of-preconditioned","slug":"a-general-regret-bound-of-preconditioned","title":"A General Regret Bound of Preconditioned Gradient Method for DNN Training","date":"2023-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"exploiting-rich-textual-user-product-context","title":"Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis","date":"2022-12-17","arxiv_id":"2212.08888","n_code_links":0,"syntology":null},{"paper":null,"slug":"budgetlongformer-can-we-cheaply-pretrain-a","title":"BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?","date":"2022-11-30","arxiv_id":"2211.17135","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-to-fine-tune-vision-models-with-sgd","title":"How to Fine-Tune Vision Models with SGD","date":"2022-11-17","arxiv_id":"2211.09359","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-algorithmic-stability-and","title":"On the Algorithmic Stability and Generalization of Adaptive Optimization Methods","date":"2022-11-08","arxiv_id":"2211.03970","n_code_links":0,"syntology":null},{"paper":"/paper/towards-fast-single-trial-online-erp-based","slug":"towards-fast-single-trial-online-erp-based","title":"Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study","date":"2022-11-04","arxiv_id":"2211.10352","n_code_links":2,"syntology":null},{"paper":null,"slug":"processing-long-legal-documents-with-pre","title":"Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer","date":"2022-11-02","arxiv_id":"2211.00974","n_code_links":0,"syntology":null},{"paper":"/paper/classactionprediction-a-challenging-benchmark","slug":"classactionprediction-a-challenging-benchmark","title":"ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US","date":"2022-11-01","arxiv_id":"2211.00582","n_code_links":1,"syntology":null},{"paper":"/paper/how-long-is-enough-exploring-the-optimal","slug":"how-long-is-enough-exploring-the-optimal","title":"How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling","date":"2022-10-25","arxiv_id":"2211.07713","n_code_links":1,"syntology":null},{"paper":"/paper/amos-an-adam-style-optimizer-with-adaptive","slug":"amos-an-adam-style-optimizer-with-adaptive","title":"Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale","date":"2022-10-21","arxiv_id":"2210.11693","n_code_links":1,"syntology":{"ran":14,"of":18,"n_ran_checked":14,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["google-research/jestimator"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"littlebird-efficient-faster-longer","title":"LittleBird: Efficient Faster & Longer Transformer for Question Answering","date":"2022-10-21","arxiv_id":"2210.11870","n_code_links":0,"syntology":null},{"paper":"/paper/idna-abf-multi-scale-deep-biological-language","slug":"idna-abf-multi-scale-deep-biological-language","title":"iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations","date":"2022-10-17","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"multi-granularity-argument-mining-in-legal","title":"Multi-granularity Argument Mining in Legal Texts","date":"2022-10-17","arxiv_id":"2210.09472","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-exploration-of-hierarchical-attention","title":"An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification","date":"2022-10-11","arxiv_id":"2210.05529","n_code_links":0,"syntology":null},{"paper":"/paper/knowledge-injected-prompt-based-fine-tuning","slug":"knowledge-injected-prompt-based-fine-tuning","title":"Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding","date":"2022-10-07","arxiv_id":"2210.03304","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["whaleloops/KEPT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/nag-gs-semi-implicit-accelerated-and-robust","slug":"nag-gs-semi-implicit-accelerated-and-robust","title":"NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer","date":"2022-09-29","arxiv_id":"2209.14937","n_code_links":2,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["skolai/nag-gs","naggsopt/naggs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-learning-and-machine-learning-for","title":"Deep learning and machine learning for Malaria detection: overview, challenges and future directions","date":"2022-09-27","arxiv_id":"2209.13292","n_code_links":0,"syntology":null},{"paper":"/paper/code-comment-inconsistency-detection-with","slug":"code-comment-inconsistency-detection-with","title":"Code Comment Inconsistency Detection with BERT and Longformer","date":"2022-07-29","arxiv_id":"2207.14444","n_code_links":1,"syntology":null},{"paper":null,"slug":"salo-an-efficient-spatial-accelerator","title":"SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences","date":"2022-06-29","arxiv_id":"2206.14550","n_code_links":0,"syntology":null},{"paper":"/paper/persian-natural-language-inference-a-meta-1","slug":"persian-natural-language-inference-a-meta-1","title":"Persian Natural Language Inference: A Meta-learning approach","date":"2022-05-18","arxiv_id":"2205.08755","n_code_links":1,"syntology":null},{"paper":"/paper/sequencer-deep-lstm-for-image-classification","slug":"sequencer-deep-lstm-for-image-classification","title":"Sequencer: Deep LSTM for Image Classification","date":"2022-05-04","arxiv_id":"2205.01972","n_code_links":5,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["okojoalg/sequencer","rwightman/pytorch-image-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"abstractive-summarization-of-hospitalisation","title":"Abstractive summarization of hospitalisation histories with transformer networks","date":"2022-04-05","arxiv_id":"2204.02208","n_code_links":0,"syntology":null},{"paper":"/paper/surrogate-gap-minimization-improves-sharpness-1","slug":"surrogate-gap-minimization-improves-sharpness-1","title":"Surrogate Gap Minimization Improves Sharpness-Aware Training","date":"2022-03-15","arxiv_id":"2203.08065","n_code_links":2,"syntology":null},{"paper":null,"slug":"the-nlp-task-effectiveness-of-long-range","title":"The NLP Task Effectiveness of Long-Range Transformers","date":"2022-02-16","arxiv_id":"2202.07856","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-adamw-through-proximal-methods-1","slug":"understanding-adamw-through-proximal-methods-1","title":"Understanding AdamW through Proximal Methods and Scale-Freeness","date":"2022-01-31","arxiv_id":"2202.00089","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/clinical-longformer-and-clinical-bigbird","slug":"clinical-longformer-and-clinical-bigbird","title":"Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences","date":"2022-01-27","arxiv_id":"2201.11838","n_code_links":1,"syntology":null},{"paper":null,"slug":"hierarchical-neural-network-approaches-for","title":"Hierarchical Neural Network Approaches for Long Document Classification","date":"2022-01-18","arxiv_id":"2201.06774","n_code_links":0,"syntology":null},{"paper":null,"slug":"simple-local-attentions-remain-competitive-1","title":"Simple Local Attentions Remain Competitive for Long-Context Tasks","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-convnet-for-the-2020s","slug":"a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","arxiv_id":"2201.03545","n_code_links":54,"syntology":{"ran":57,"of":80,"n_ran_checked":49,"n_instrument":8,"unverified":23,"pointer_only":12,"phrase":"57 ran (of which 39 constructed an object rather than computing a result; 49 with no instrument failure: 1 honoured, 0 violated, 48 with no contract checked; 8 where Syntology's instrument failed) · 23 unverified","official":{"repos":["facebookresearch/ConvNeXt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/scrolls-standardized-comparison-over-long","slug":"scrolls-standardized-comparison-over-long","title":"SCROLLS: Standardized CompaRison Over Long Language Sequences","date":"2022-01-10","arxiv_id":"2201.03533","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":4,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["tau-nlp/scrolls"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"accelerating-neural-network-optimization","title":"Accelerating Neural Network Optimization Through an Automated Control Theory Lens","date":"2022-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/keyphrase-generation-beyond-the-boundaries-of","slug":"keyphrase-generation-beyond-the-boundaries-of","title":"Keyphrase Generation Beyond the Boundaries of Title and Abstract","date":"2021-12-13","arxiv_id":"2112.06776","n_code_links":1,"syntology":null},{"paper":null,"slug":"extending-adamw-by-leveraging-its-second","title":"Extending AdamW by Leveraging Its Second Moment and Magnitude","date":"2021-12-09","arxiv_id":"2112.06125","n_code_links":0,"syntology":null},{"paper":"/paper/plsum-generating-pt-br-wikipedia-by","slug":"plsum-generating-pt-br-wikipedia-by","title":"PLSUM: Generating PT-BR Wikipedia by Summarizing Multiple Websites","date":"2021-12-02","arxiv_id":"2112.01591","n_code_links":1,"syntology":null},{"paper":null,"slug":"new-approaches-to-long-document-summarization","title":"New Approaches to Long Document Summarization: Fourier Transform Based Attention in a Transformer Model","date":"2021-11-25","arxiv_id":"2111.15473","n_code_links":0,"syntology":null},{"paper":null,"slug":"marcqap-effective-context-modeling-for","title":"MarCQAp: Effective Context Modeling for Conversational Question Answering","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/primer-pyramid-based-masked-sentence-pre","slug":"primer-pyramid-based-masked-sentence-pre","title":"PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization","date":"2021-10-16","arxiv_id":"2110.08499","n_code_links":3,"syntology":{"ran":7,"of":7,"n_ran_checked":4,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["allenai/primer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/an-exploratory-study-on-long-dialogue","slug":"an-exploratory-study-on-long-dialogue","title":"An Exploratory Study on Long Dialogue Summarization: What Works and What's Next","date":"2021-09-10","arxiv_id":"2109.04609","n_code_links":1,"syntology":null},{"paper":null,"slug":"bumblebee-a-transformer-for-music","title":"BumbleBee: A Transformer for Music","date":"2021-07-07","arxiv_id":"2107.03443","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-transformer-for-direct-speech","title":"Efficient Transformer for Direct Speech Translation","date":"2021-07-07","arxiv_id":"2107.03069","n_code_links":0,"syntology":null},{"paper":"/paper/what-helps-transformers-recognize","slug":"what-helps-transformers-recognize","title":"What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition","date":"2021-07-05","arxiv_id":"2107.02294","n_code_links":1,"syntology":null},{"paper":"/paper/ranger21-a-synergistic-deep-learning","slug":"ranger21-a-synergistic-deep-learning","title":"Ranger21: a synergistic deep learning optimizer","date":"2021-06-25","arxiv_id":"2106.13731","n_code_links":2,"syntology":null},{"paper":"/paper/self-supervised-document-similarity-ranking","slug":"self-supervised-document-similarity-ranking","title":"Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference","date":"2021-06-02","arxiv_id":"2106.01186","n_code_links":1,"syntology":null},{"paper":"/paper/citeworth-cite-worthiness-detection-for","slug":"citeworth-cite-worthiness-detection-for","title":"CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding","date":"2021-05-23","arxiv_id":"2105.10912","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-learning-with-swin","slug":"self-supervised-learning-with-swin","title":"Self-Supervised Learning with Swin Transformers","date":"2021-05-10","arxiv_id":"2105.04553","n_code_links":6,"syntology":null},{"paper":null,"slug":"nlp-iis-ut-at-semeval-2021-task-4-machine","title":"NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer","date":"2021-05-08","arxiv_id":"2105.03775","n_code_links":0,"syntology":null},{"paper":"/paper/text-guide-improving-the-quality-of-long-text","slug":"text-guide-improving-the-quality-of-long-text","title":"Text Guide: Improving the quality of long text classification by a text selection method based on feature importance","date":"2021-04-15","arxiv_id":"2104.07225","n_code_links":1,"syntology":null},{"paper":"/paper/nlquad-a-non-factoid-long-question-answering","slug":"nlquad-a-non-factoid-long-question-answering","title":"NLQuAD: A Non-Factoid Long Question Answering Data Set","date":"2021-04-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/2103-15358","slug":"2103-15358","title":"Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding","date":"2021-03-29","arxiv_id":"2103.15358","n_code_links":3,"syntology":{"ran":8,"of":10,"n_ran_checked":5,"n_instrument":3,"unverified":2,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["microsoft/vision-longformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"a-practical-survey-on-faster-and-lighter","title":"A Practical Survey on Faster and Lighter Transformers","date":"2021-03-26","arxiv_id":"2103.14636","n_code_links":0,"syntology":null},{"paper":"/paper/swin-transformer-hierarchical-vision","slug":"swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","arxiv_id":"2103.14030","n_code_links":80,"syntology":{"ran":123,"of":207,"n_ran_checked":82,"n_instrument":41,"unverified":84,"pointer_only":45,"phrase":"123 ran (of which 45 constructed an object rather than computing a result; 82 with no instrument failure: 5 honoured, 2 violated, 75 with no contract checked; 41 where Syntology's instrument failed) · 84 unverified","official":{"repos":["microsoft/Swin-Transformer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"paper":"/paper/identifying-machine-paraphrased-plagiarism","slug":"identifying-machine-paraphrased-plagiarism","title":"Identifying Machine-Paraphrased Plagiarism","date":"2021-03-22","arxiv_id":"2103.11909","n_code_links":2,"syntology":null},{"paper":null,"slug":"neural-transfer-learning-with-transformers","title":"Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis","date":"2021-02-03","arxiv_id":"2102.02111","n_code_links":0,"syntology":null},{"paper":null,"slug":"grid-search-hyperparameter-benchmarking-of","title":"Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC","date":"2021-01-15","arxiv_id":"2101.06326","n_code_links":0,"syntology":null},{"paper":"/paper/cross-document-language-modeling","slug":"cross-document-language-modeling","title":"CDLM: Cross-Document Language Modeling","date":"2021-01-02","arxiv_id":"2101.00406","n_code_links":2,"syntology":null},{"paper":"/paper/training-data-efficient-image-transformers","slug":"training-data-efficient-image-transformers","title":"Training data-efficient image transformers & distillation through attention","date":"2020-12-23","arxiv_id":"2012.12877","n_code_links":40,"syntology":{"ran":12,"of":19,"n_ran_checked":9,"n_instrument":3,"unverified":7,"pointer_only":3,"phrase":"12 ran (of which 1 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 7 unverified","official":{"repos":["facebookresearch/deit"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/pre-training-protein-language-models-with","slug":"pre-training-protein-language-models-with","title":"Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks","date":"2020-12-05","arxiv_id":"2012.03084","n_code_links":1,"syntology":null},{"paper":"/paper/debatesum-a-large-scale-argument-mining-and","slug":"debatesum-a-large-scale-argument-mining-and","title":"DebateSum: A large-scale argument mining and summarization dataset","date":"2020-11-14","arxiv_id":"2011.07251","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Hellisotherpeople/DebateSum","Hellisotherpeople/debate2vec","arvind-balaji/debate-cards"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/longformer-for-ms-marco-document-re-ranking","slug":"longformer-for-ms-marco-document-re-ranking","title":"Longformer for MS MARCO Document Re-ranking Task","date":"2020-09-20","arxiv_id":"2009.09392","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-transformers-a-survey","title":"Efficient Transformers: A Survey","date":"2020-09-14","arxiv_id":"2009.06732","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tune-longformer-for-jointly-predicting","title":"Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity","date":"2020-07-15","arxiv_id":"2007.07803","n_code_links":0,"syntology":null},{"paper":"/paper/document-classification-for-covid-19","slug":"document-classification-for-covid-19","title":"Document Classification for COVID-19 Literature","date":"2020-06-15","arxiv_id":"2006.13816","n_code_links":1,"syntology":null},{"paper":"/paper/adahessian-an-adaptive-second-order-optimizer","slug":"adahessian-an-adaptive-second-order-optimizer","title":"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning","date":"2020-06-01","arxiv_id":"2006.00719","n_code_links":4,"syntology":{"ran":5,"of":11,"n_ran_checked":4,"n_instrument":1,"unverified":6,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["amirgholami/adahessian"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"7cdc72364998cd70cf9279c4fd7fb77976150e7a750c04a499d864490ae9a9eb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}